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    AI Coding Agent Setup: 3 Steps to Run a Full Research Workflow from Scratch

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    This article explains how researchers and clinicians without prior agent setup experience can set up an AI coding agent in three steps and use it across a full research workflow. It covers environment and network self-checks, credential storage and connectivity validation, project rule configuration, and practical applications in literature management, statistical analysis, manuscript drafting, and pre-submission verification. It also highlights compliance, reproducibility, cost control, and human review.

    1. The Automation Problem: What “Automatable Labor” Looks Like in Research

    Many graduate students, PhD researchers, and clinicians spend a large part of their time on tasks that are clearly structured, low in judgment difficulty, but still easy to get wrong:

    • Searching literature item by item, removing duplicates, exporting bibliographic records, and manually standardizing reference formats;
    • Repeatedly reworking descriptive statistics, group comparisons, regression models, result tables, and figures;
    • Rewriting the same methods section according to different journal templates, adjusting headings and numbering;
    • Before submission, checking whether table and figure values match the raw data, and whether cited references actually support the claims.

    These tasks share a common feature: the input and output boundaries are clear, the rules are enumerable, and errors can be checked. This is exactly the area where AI coding agents—agents that can read and write files in a terminal or IDE, execute commands, and iteratively correct errors as needed—perform well.

    It is important to clarify one point: an AI coding agent is not simply “a more talkative assistant.” Its key difference is that it has file-system access and command execution permissions. Therefore, it can handle a closed loop of “write script → run → inspect errors → fix,” rather than only producing code that cannot be verified.

    2. Why “Follow the Million-View Tutorial” Still Fails

    Most failures are not caused by incorrect operations. They are caused by the fact that tutorials implicitly assume prerequisites that the author has already completed. There are four common blockers:

    1. Missing environment prerequisites: No general JavaScript runtime or package manager is installed, no terminal permissions are available, and installation commands fail.
    2. Missing network prerequisites: Agent requests are long-lived streaming requests. Many terminals and IDEs do not use the system proxy by default, which shows up as “connection timeout” or “stream interruption.” The proxy must be set to global/TUN mode, otherwise terminal traffic will bypass the proxy.
    3. Missing credential validation: Users only paste in a key but never check whether the key is valid, whether the quota is sufficient, or what return code was returned. As a result, every failure looks like “the tool is broken.”
    4. Permission and directory confusion: Commands are executed outside the project root, and the agent may modify raw data without permission. Later, it becomes impossible to trace what was changed.

    The correct approach is not to copy commands, but to build a self-checkable minimal closed loop using three stages: environment → credentials → integration.

    3. Three-Step Setup: The Minimal Working Loop

    Step 0: Environment and Network Self-Check

    1. # Check whether the runtime and package manager exist; install a general runtime if missing
    2. node -v || echo "Runtime is required"
    3. npm -v  || echo "Package manager is required"
    5. # Verify whether the terminal is actually using the proxy (should return public egress information)
    6. curl -s --max-time 10 https://api.ipify.org; echo

    If curl times out, the problem is in the network layer and is unrelated to the agent. Switch the proxy to global/TUN mode and try again.

    Step 1: Obtain and Store Model Access Credentials

    Every agent inference consumes tokens and is billed by usage. Credentials should be obtained through compliant channels: an institution-approved model service channel, or a public cloud model service platform such as Baidu AI Cloud, where access keys can be created in the console. Third-party resale channels may involve quota and data-compliance risks and are not recommended for research data containing patient information.

    Three hard rules for credential management:

    • Store credentials only in environment variables or the system keyring. Do not put them in repositories, paper appendices, or screenshots.
    • Set usage limits and alerts to prevent complex tasks—such as full codebase analysis or long-document processing—from unexpectedly increasing costs.
    • Use separate keys for different purposes. Use different keys for routine test runs and batch tasks so abnormal consumption can be located more easily.
    1. # Write credentials into the current session environment (field names are illustrative; follow the chosen tool's documentation)
    2. export MODEL_API_KEY="<your-key>"
    3. export MODEL_BASE_URL="https://<gateway-endpoint>/v1"

    Step 2: Integrate the Key and Verify Connectivity

    Most agents can read credentials from a configuration file or environment variables. The key point is to verify connectivity before starting real work.

    1. // ~/.agent/config.json (structure illustrative)
    2. {
    3.   "model": "default",
    4.   "apiKeyEnv": "MODEL_API_KEY",
    5.   "baseUrl": "https://<gateway-endpoint>/v1",
    6.   "permissionMode": "ask",
    7.   "maxTokens": 8192
    8. }
    1. # Minimal connectivity test: verify key, network, and model availability in one request
    2. curl -s "$MODEL_BASE_URL/chat/completions" \
    3.   -H "Authorization: Bearer $MODEL_API_KEY" \
    4.   -H "Content-Type: application/json" \
    5.   -d '{"messages":[{"role":"user","content":"ping"}],"max_tokens":16}' | head -c 400

    If the response is a normal JSON structure, the chain is working. A 401 response indicates a key problem, a timeout indicates a network problem, and an insufficient-quota response means you need to top up or switch to a channel within your allocation.

    Step 3: Install in the IDE Terminal and Define Project Rules

    In an editor with a built-in terminal, you can use the tool’s own installer commands to complete deployment and avoid manually editing environment variables. What really determines success is giving the agent a project rule file that embeds research norms into the context and reduces repeated corrections.

    1. # AGENTS.md (place in the project root)
    3. ## Goal
    4. Provide statistical analysis and methodology drafting support for an outpatient follow-up cohort.
    6. ## Directory conventions
    7. - data/raw/    Raw data; read-only; must not be modified or deleted under any circumstances
    8. - data/derived/ Cleaned derived data
    9. - scripts/     Reproducible scripts, numbered sequentially
    10. - results/     Table and figure outputs
    11. - docs/        Manuscript and response letters
    13. ## Prohibitions
    14. - Must not access the network to upload any data files
    15. - Must not modify any files under data/raw/
    16. - Before deleting or refactoring scripts under scripts/, state the impact first
    18. ## Statistical rules
    19. - For continuous variables, test normality and homogeneity of variance before choosing parametric or non-parametric methods
    20. - Report effect sizes and 95% confidence intervals, not only p-values
    21. - Correct for multiple comparisons and state the correction method

    With this file, the agent’s output will shift from “generic advice” to “executable plans that comply with your domain standards.”

    4. Applying the Agent to Research Scenarios

    4.1 Literature Search and Bibliography Management

    An agent can write scripts that call public literature database APIs or parse exported bibliography files to deduplicate records, cluster them by topic, extract methods and sample sizes, and generate comparison tables. The key is to make the agent output intermediate products for your review, rather than directly providing conclusions.

    “Read refs/*.ris, stratify by study design, and output a summary table of sample size and primary endpoint for each design type; list duplicate records and the basis for deduplication.”

    4.2 Data Cleaning, Statistics, and Visualization

    Write “analysis” as a pipeline, not as a one-off script:

    1. # Example target directory structure
    2. scripts/01_clean.py -> data/derived/cohort_clean.csv
    3. scripts/02_describe.py -> results/table1.tex
    4. scripts/03_model.py -> results/models.json + results/fig2.pdf
    1. # Let the agent follow the order: “check assumptions first, then choose methods”
    2. import json, pandas as pd
    3. from scipy import stats
    5. df = pd.read_csv("data/derived/cohort_clean.csv")
    6. groups = [g["value"].dropna().values for _, g in df.groupby("arm")]
    8. normal = all(stats.shapiro(g).pvalue > 0.05 for g in groups if len(g) >= 3)
    9. result = (stats.f_oneway(*groups) if normal
    10.           else stats.kruskal(*groups))
    12. json.dump({"test": "anova" if normal else "kruskal",
    13.            "statistic": float(result.statistic),
    14.            "p_value": float(result.pvalue),
    15.            "n_per_group": [len(g) for g in groups]},
    16.           open("results/models.json", "w"), ensure_ascii=False, indent=2)

    The agent is good at extending this kind of skeleton into multiple models, subgroup analyses, sensitivity analyses, and figure refinement. You are responsible for confirming variable definitions, missing-value handling, and whether the research question is correctly matched.

    4.3 Manuscript Writing Support

    The bottleneck in review writing is not language, but structure and information organization. It is better to first have the agent produce a structured outline and information matrix, then expand section by section:

    • Input the topic and the filtered reference list, and ask for a three-dimensional matrix of “controversy points — evidence strength — research gaps”;
    • When writing section by section, require every claim to include a citation number; if no source is available, mark it as TODO;
    • During paraphrasing and language polishing, require that “all data and citations are preserved, and only sentence structure and information density are adjusted” to prevent numeric drift during rewriting.

    4.4 Claim Traceability and Pre-Submission Verification

    This is one of the most valuable and most easily overlooked uses of an agent. You can ask it to build a “claim — evidence” checklist:

    1. # Extract cited sentences from the manuscript, compare them with the bibliography/evidence database, and mark items that cannot be traced
    2. import re, json
    4. text = open("docs/manuscript.md", encoding="utf-8").read()
    5. claims = re.findall(r"[^.\n]*\[\d+(?:[-,]\d+)*\][^.\n]*\.", text)
    7. report = [{"claim": c.strip(),
    8.            "refs": re.findall(r"\d+", c),
    9.            "status": "Requires manual verification of whether the source supports the statement"}
    10.           for c in claims]
    12. json.dump(report, open("docs/claim_check.json", "w"),
    13.           ensure_ascii=False, indent=2)
    14. print(f"Found {len(report)} cited claims that require verification.")

    Similarly, before submission, the agent can check data consistency item by item: manuscript values ↔ result JSON ↔ figure source data ↔ raw data, and output a discrepancy list. The agent can locate inconsistencies, but whether they are real must still be judged by you. This step cannot be outsourced.

    5. Risk Boundaries and Usage Discipline

    • Data compliance: For tasks involving patient or participant data, de-identify the data first. Prefer compliant channels that do not use your data for training, and follow institutional ethics and data-management rules.
    • Hallucinations cannot be eliminated: All literature, data, and formula conclusions must be traceable to verifiable sources. A reference that cannot be traced to a verifiable source should be treated as nonexistent.
    • Reproducibility first: Run git commit before making changes, let the agent work in a clean workspace, and allow one-click rollback if problems occur.
    • Cost and rate limits: Process long documents in segments, run small-sample trials first, and set a maximum number of iterations for complex tasks to avoid burning tokens in invalid loops.
    • Human in the loop: Keep high-risk actions—deleting files, modifying raw data, batch renaming, and external sending—in “ask every time” permission mode.

    6. Troubleshooting Checklist

    Symptom First Check Resolution Direction
    Installation command returns “command not found” Runtime/package manager version Install the runtime environment and reopen the terminal to refresh PATH
    Connection timeout or stream interruption Whether the proxy is in global/TUN mode Switch to global mode and retry curl
    401/403 Key validity; whether Base URL includes a version path Use the minimal connectivity test to locate the issue
    Quota or rate-limit error Usage limit and number of concurrent tasks Reduce concurrency, segment tasks, and add quota
    Agent modifies files without permission Permission mode and project rule file Switch to ask mode and add missing prohibitions
    Results do not match raw data Whether the “raw is read-only” directory convention was followed Rerun the pipeline and compare intermediate products

    7. Conclusion

    The value of a research agent is not that it thinks for you, but that it compresses labor with clear rules and verifiable outputs to near-zero cost. The three-step setup is only the entry ticket: the environment self-check determines whether it can run, credential custody determines whether it runs safely, and project rules plus reproducible pipelines determine whether the output is usable.

    Once these three things are solid, start with high-frequency tasks such as literature management, statistical analysis, and claim verification. Then you can reallocate your time to the work that truly requires judgment: raising questions, designing studies, and interpreting results.

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